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Record W1480974392 · doi:10.1177/160940690200100405

The Pink Elephant Paradox (or, Avoiding the Misattribution of Data)

2002· article· en· W1480974392 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMisattribution of memoryPhenomenonEpistemologyQualitative researchSociologyPsychologyPhilosophySocial science

Abstract

fetched live from OpenAlex

The pink elephant paradox refers to the threat to inductive thinking caused by the difficulty of inadvertently proving the existence of a concept or phenomena just because it overtly or insidiously exists in one's thoughts, leading to misattribution, or miscategorization of data, and thus subverting inductive processes. As Morse and Mitcham discussed in Part I, this is reduced through inductive strategies, including processes of saturation, replication, and verification. In this article, I present a story of how the phenomenon of interest in nurse-patient interaction evolved and emerged through a number of qualitative projects. At each stage, concepts were identified, explored, and developed in order to more elucidate the central phenomenon. I will show how, while at times I could identify and avoid the pink elephant, at other times there were one or a herd lurking in the shadows or rampaging through my work. I think that discussing both the successes and pit falls is one way to acknowledge and address the fact that, although we accept the evolution in ideas and thought processes in qualitative research, we still may not be comfortable in articulating the far more complex and insidious threats to inductive processes. Some schools of qualitative inquiry consider analysis of the literature a hindrance—in fact an invalidity—before commencing fieldwork. To the contrary, when a researcher is studying a concept rather than letting a concept emerge from a setting, it is essential to undertake a thorough theoretical and conceptual analysis of the literature (Morse, 2000; Morse et al, 1996). In my own program of research, the concept analysis was a study in, and of, itself, with the purpose of examining the maturity of concepts, and the explicit and implicit theoretical and research models. The literature constituted data that could be analyzed and formed the basis for a reconceptualization of the original concept by contrasting it with the theory derived from the fieldwork studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.579
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0830.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.898
GPT teacher head0.747
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it